| from typing import List, Tuple | |
| import nltk | |
| import sklearn | |
| from tfidf import TfidfWikiGuesser | |
| import numpy as np | |
| import pandas as pd | |
| class QuizBowlModel: | |
| def __init__(self): | |
| """ | |
| Load your model(s) and whatever else you need in this function. | |
| Do NOT load your model or resources in the guess_and_buzz() function, | |
| as it will increase latency severely. | |
| """ | |
| #best accuracy when using wiki_page_text.json | |
| self.guesser = TfidfWikiGuesser(wikidump=None) #can specify different wikidump if needed | |
| print("model loaded") | |
| def guess_and_buzz(self, question_text: List[str]) -> List[Tuple[str, bool]]: | |
| """ | |
| This function accepts a list of question strings, and returns a list of tuples containing | |
| strings representing the guess and corresponding booleans representing | |
| whether or not to buzz. | |
| So, guess_and_buzz(["This is a question"]) should return [("answer", False)] | |
| If you are using a deep learning model, try to use batched prediction instead of | |
| iterating using a for loop. | |
| """ | |
| answers = [] | |
| top_guesses = 3 #guesser will return this amount guesses for each question (in sorted confidence) | |
| for question in question_text: | |
| guesses = self.guesser.make_guess(question, num_guesses=top_guesses) | |
| #print(guesses) | |
| #do the buzzing | |
| #make a tuple and add to answers list | |
| tup = (guesses[0], True) | |
| answers.append(tup) | |
| return answers | |